“带状疱疹优秀病例征文”病例征集通知

<正>带状疱疹是一种急性病毒性感染性皮肤病,并可导致严重的神经痛等各种并发症。如何凭借最佳治疗策略达到缩短病程,预防感染、降低并发症等目的,需要临床医师不断积累临床经验并优化治疗方法。因此,《中国真菌学杂志》计划于2015~2016年在全国范围内开展"带状疱疹优秀病例征文"。项目以     

“带状疱疹优秀病例征文”病例征集通知

<正>带状疱疹是一种急性病毒性感染性皮肤病,并可导致严重的神经痛等各种并发症。如何凭借最佳治疗策略达到缩短病程,预防感染、降低并发症等目的,需要临床医师不断积累临床经验并优化治疗方法。因此,《中国真菌学杂志》计划于2015~2016年在全国范围内开展"带状疱疹优秀病例征文"。项目以     

“带状疱疹优秀病例征文”病例征集通知

<正>带状疱疹是一种急性病毒性感染性皮肤病,并可导致严重的神经痛等各种并发症。如何凭借最佳治疗策略达到缩短病程,预防感染、降低并发症等目的,需要临床医师不断积累临床经验并优化治疗方法。因此,《中国真菌学杂志》计划于2015~2016年在全国范围内开展"带状疱疹优秀病例征文"。项目以真实病例为蓝本,通过病例征集为临床医师制定个体化治疗方案提供借鉴,为优化治疗方案提供循证医学证据。促进和推动我国临床医生在该领域的学术水平和诊疗水平。     

“带状疱疹优秀病例征文”病例征集通知

<正>带状疱疹是一种急性病毒性感染性皮肤病,并可导致严重的神经痛等各种并发症。如何凭借最佳治疗策略达到缩短病程,预防感染、降低并发症等目的,需要临床医师不断积累临床经验并优化治疗方法。因此,《中国真菌学杂志》计划于2015~2016年在全国范围内开展"带状疱疹优秀病例征文"。项目以真实病例为蓝本,通过病例征集为临床医师制定个体化治疗方案提供借鉴,为优化治疗方案提供循证医学证据。促进和推动我国临床医生在该领域的学术水平和诊疗水平。     

“带状疱疹优秀病例征文”病例征集通知

<正>带状疱疹是一种急性病毒性感染性皮肤病,并可导致严重的神经痛等各种并发症。如何凭借最佳治疗策略达到缩短病程,预防感染、降低并发症等目的,需要临床医师不断积累临床经验并优化治疗方法。因此,《中国真菌学杂志》计划于2015~2016年在全国范围内开展"带状疱疹优秀病例征文"。项目以真实病例为蓝本,通过病例征集为临床医师制定个体化治疗方案提供借鉴,为优化治疗方案提供循证医学证据。促进和推动我国临床医生在该领域的学术水平和诊疗水平。     

关于质体随细胞分裂传递的数学模型

本文根据质体是具有遗传功能的细胞器这一事实出发,对质体传递规律首次进行了定量研究,建立了突变质体在细胞分裂中传递的全概率和条件概率公式．并对此公式的应用作了进一步的探讨．本文所提供的方法将为质体遗传开辟一条定量研究的新路．     

基于结构信息的RNA多序列比对

多序列比对是生物信息学中重要的基础研究内容,对各种RNA序列分析方法而言,这也是非常重要的一步。不像DNA和蛋白质,许多功能RNA分子的序列保守性要远差于其结构的保守性,因此,对RNA的分析研究要求其多序列比对不仅要考虑序列信息,而且要充分考虑到其结构信息。本文提出了一种考虑了结构信息的同源RNA多序列比对算法,它先利用热力学方法计算出每条序列的配对概率矩阵,得到结构信息,由此构造各条序列的结构信息矢量,结合传统序列比对方法,提出优化目标函数,采用动态规划算法和渐进比对得到最后的多序列比对。试验证实该方法的有效性。     

海南岛农业结构的生态经济系统分析

本文提出了一个综合考虑生态与经济的农业结构优化模型。以海南岛作一实例对农业结构系统分析中的模拟方法进行了具体研究。这个模型应用来为海南岛的农业计划和管理提供具体信息。用FELIXC-512计算机求出了20个海南岛农业结构的优化方案,并对其生态经济效果作了论证。     

武汉生物工程学院获批“荆楚卓越工程师协同育人计划”项目

正12月16日,湖北省教育厅公布了2016年度湖北省普通本科高校"荆楚卓越人才"协同育人计划项目名单,我校制药工程专业获批2016年度"荆楚卓越工程师协同育人计划"项目。我校制药工程专业"荆楚卓越工程师协同育人计划"项目,通过固化协同育人机制,建立卓越人才培养标准体系,优化课程体系内容,创新教学方式方法,强化实践教学环节,建设双师型教师队伍,促进专业链     

个体运动的病毒传播行为模拟

提出了一个基于二维规则网格的SIS(Susceptible-Infected-Susceptible)动态疾病传播模型,并用元胞自动机方法进行计算机模拟,考查该模型中感染概率、治愈概率、人群密度及人群的流动对疾病传播的影响,结果表明,系统的稳态感染比例随感染概率和人群密度的增加而增大,随治愈概率的增加而减小,同等条件下流动人群比静止人群更容易传播疾病,根据这些研究结论最后给出了对应的疾病预防和控制措施。     

Estimation of breed contributions to present and future genetic diversity of 44 North Eurasian cattle breeds using core set diversity measures

Extinction of breeds threatens genetic diversity of livestock species. The need to conserve genetic diversity is widely accepted but involves in general two questions: (i) is the expected loss of diversity in a set of breeds within a defined future time horizon large enough to establish a conservation plan, and if so (ii) which breeds should be prioritised for such a conservation plan? The present study uses a marker assisted methodology to address these questions. The methodology combines core set diversity measures with a stochastic method for the estimation of expected future diversity and breed marginal diversities. The latter is defined as the change in the total diversity of all breeds caused by a one unit decrease in extinction probability of a particular breed. The stochastic method was validated by means of simulations. A large field data set consisting of 44 North Eurasian cattle breeds was analysed using simplified determined extinction probabilities. The results show that the expected loss of diversity in this set within the next 20 to 50 years is between 1 and 3% of the actual diversity, provided that the extinction probabilities which were used are approximately valid. If this loss is to be reduced, it is sufficient to include those three to five breeds with the highest marginal diversity in a conservation scheme.     

Evaluation of alternative parameter settings for dose restoration and full plan adaptation in IMPT for prostate cancer

Background/purposeIntensity-modulated proton therapy is highly sensitive to anatomical variations. A dose restoration method and a full plan adaptation method have been developed earlier, both requiring several parameter settings. This study evaluates the validity of the previously selected settings by systematically comparing them to alternatives.Materials/methodsThe dose restoration method takes a prior plan and uses an energy-adaptation followed by a spot-intensity re-optimization to restore the plan to its initial state. The full adaptation method uses an energy-adaptation followed by the addition of new spots and a spot-intensity optimization to fit the new anatomy. We varied: 1) The margins and robustness settings of the prior plan, 2) the spot-addition sample size, i.e. the number of added spots, 3) the spot-addition stopping criterion, and 4) the spot-intensity optimization approach. The last three were evaluated only for the full plan adaptation. Evaluations were done on 88 CT scans of 11 prostate cancer patients. Dose was prescribed as 55 Gy(RBE) to the lymph nodes and seminal vesicles with a boost to 74 Gy(RBE) to the prostate.ResultsFor the dose restoration method, changing the applied CTV-to-PTV margins and plan robustness in the prior plans yielded insufficient target coverage or increased OAR doses. For the full plan adaptation, more spot-addition iterations and using a different optimization approach resulted in lower OAR doses compared to the default settings while maintaining target coverage. However, the calculation times increased by up to 20 times, making these variations infeasible for online-adaptation.ConclusionWe recommend maintaining the default setting for the dose restoration approach. For the full plan adaptation we recommend to focus on fine-tuning the optimization-parameters, and apart from this using the default settings.     

SOME ASPECTS OF THE MANAGEMENT OF BILATERAL CALCULUS DISEASE OF THE KIDNEY

In the diagnosis of bilateral calculus disease of the kidneys, it is important to differentiate between cystine, uric acid, calcium oxalate and phosphate renal lithiasis. Methods for distinguishing one from another are described.Dietary therapy is the method of choice for cystine and uric acid lithiasis.In calcium and phosphate urolithiasis, dietary therapy is a very useful adjunct. It must be regulated by careful studies of its effect on urinary calcium precipitability, a new test for which is described based upon the demonstration of the existence of two forms of calcium in the urine.Irrigation therapy for calcium phosphate and phosphate lithiasis is briefly discussed.Surgical therapy for large renal phosphatic calculi is discussed to show how considerations of renal counterbalance and urinary calcium, magnesium and phosphate excretion through damaged kidney substances influence the surgical plan in each case.     

The empirical coverage of confidence intervals: Point estimates and confidence intervals for confidence levels

Many confidence intervals calculated in practice are potentially not exact, either because the requirements for the interval estimator to be exact are known to be violated, or because the (exact) distribution of the data is unknown. If a confidence interval is approximate, the crucial question is how well its true coverage probability approximates its intended coverage probability. In this paper we propose to use the bootstrap to calculate an empirical estimate for the (true) coverage probability of a confidence interval. In the first instance, the empirical coverage can be used to assess whether a given type of confidence interval is adequate for the data at hand. More generally, when planning the statistical analysis of future trials based on existing data pools, the empirical coverage can be used to study the coverage properties of confidence intervals as a function of type of data, sample size, and analysis scale, and thus inform the statistical analysis plan for the future trial. In this sense, the paper proposes an alternative to the problematic pretest of the data for normality, followed by selection of the analysis method based on the results of the pretest. We apply the methodology to a data pool of bioequivalence studies, and in the selection of covariance patterns for repeated measures data.     

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning

PurposeTo develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method.MethodsThe data set comprised the data of 107 patients who underwent radiotherapy. Fifteen patients with dental fillings were used as the test data set. The computed tomography (CT) images of the remaining 92 patients were divided into two domains: the metal artifact and artifact-free domains. CycleGAN was used for domain translation. The artifact index of the DL-MAR images was compared with that of the original uncorrected (UC) CT images. The dose distributions of the DL-MAR and UC plans were created by comparing the reference clinical plan with the water density override method (water plan) in each dataset. Dosimetric deviation in the oral cavity from the water plan was evaluated.ResultsThe artifact index of the DL-MAR images was significantly smaller than that of the UC images in all patients (13.2 ± 4.3 vs. 267.3 ± 113.7). Compared with the reference water plan, dose differences of the UC plans were greater than those of the DL-MAR plans. DL-MAR images provided dosimetric results that were more similar to those of the water plan than the UC plan.ConclusionsWe developed a fast DL-MAR method using CycleGAN for head and neck IMRT. The proposed method could provide consistent dose calculation against metal artifact and improve the efficiency of the planning process by eliminating manual delineation.     

Clinical and anorectal motility features in chronically constipated children

Constipation is a common problem in children. It can be a symptom of functional disorder in majority of cases, but sometimes a serious organic disease is a cause of constipation. Anorectal manometry plays an important role in the diagnostic procedure. It can be presumed that some values of manometric parameters could be very useful in treatment plan. The aim of this study was to confirm or exclude such limits in manometric finding, to make a plan of the therapy more accurate. Therefore the group of 81 constipated children was investigated. There were 58 patients suffering from functional constipation and 23 with organic disorders. The age of the patients was 3 to 15 years. Scoring system was applied to estimate severity of constipation and anorectal manometry was performed. Our results did not show significant correlation between clinical symptoms and signs and the values that we obtained using anorectal manometry. But, we recommend anorectal manometry because it is a safe method and contributes to clarify the diagnosis, which is very important for treatment plan.     

An iterative approach to plan combination in radiotherapy

Radiation therapy concerns the delivery of a proper dose of radiation to a tumor volume without causing irreparable damage to surrounding healthy tissue and critical organs. The problem of plan combination in radiation therapy treatment planning (RTTP) proposed, formulated and studied here, addresses a situation when for a specific clinical case, a set of several treatment plans is proposed, but each one of them violates the prescribed dose in at least one significant region of the volume that has to be treated. We represent treatment plans as vectors in the Euclidean space, and define their equivalence, acceptability and realizability. A simple linear algebraic model for combining them is then used in order to derive, from the given set of approximate plans, a combined treatment plan, which will be both acceptable, and technically realizable. In the event that such a combined plan does not exist, the alternatives for relaxing the treatment requirements can be systematically considered.     

SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes

This paper proposes dynamic treatment regimes (DTRs) as effective individualized treatment strategies for managing chronic periodontitis. The proposed DTRs are studied via SMARTp —a two-stage sequential multiple assignment randomized trial (SMART) design. For this design, we propose a statistical analysis plan and a novel cluster-level sample size calculation method that factors in typical features of periodontal responses such as non-Gaussianity, spatial clustering, and nonrandom missingness. Here, each patient is viewed as a cluster, and a tooth within a patient's mouth is viewed as an individual unit inside the cluster, with the tooth-level covariance structure described by a conditionally autoregressive structure. To accommodate possible skewness and tail behavior, the tooth-level clinical attachment level (CAL) response is assumed to be skew-t, with the nonrandomly missing structure captured via a shared parameter model corresponding to the missingness indicator. The proposed method considers mean comparison for the regimes with or without sharing an initial treatment, where the expected values and corresponding variances or covariance for the sample means of a pair of DTRs are derived by the inverse probability weighting and method of moments. Simulation studies are conducted to investigate the finite-sample performance of the proposed sample size formulas under a variety of outcome-generating scenarios. An R package SMARTp implementing our sample size formula is available at the Comprehensive R Archive Network for free download.     

Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms

In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle’s location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle’s location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy – the crossover and mutation operator hybrid approach – is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6 MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human planner intervention. A comparison of the results with the optimized solution obtained using a similar optimization model but with human planner intervention revealed that the proposed algorithm produced optimized plans superior to that developed using the manual plan. The proposed algorithm can generate admissible solutions within reasonable computational times and can be used to develop fully automated IMRT treatment planning methods, thus reducing human planners’ workloads during iterative processes.     

Intelligence-guided beam angle optimization in treatment planning of intensity-modulated radiation therapy

An intelligence guided approach based on fuzzy inference system (FIS) was proposed to automate beam angle optimization in treatment planning of intensity-modulated radiation therapy (IMRT). The model of FIS is built on inference rules in describing the relationship between dose quality of IMRT plan and irradiated region of anatomical structure. Dose quality of IMRT plan is quantified by the difference between calculated and constraint doses of the anatomical structures in an IMRT plan. Irradiated region of anatomical structure is characterized by the metric, covered region of interest, which is the region of an anatomical structure under radiation field while beam’s eye-view is conform to target volume. Initially, an IMRT plan is created with a single beam. The dose difference is calculated for the input of FIS and the output of FIS is obtained with processing of fuzzy inference. Later, a set of candidate beams is generated for replacing the current beam. This process continues until no candidate beams is found. Then the next beam is added to the IMRT plan and optimized in the same way as the previous beam. The new beam keeps adding to the IMRT plan until the allowed beam number is reached. Two spinal cases were investigated in this study. The preliminary results show that dose quality of IMRT plans achieved by this approach is better than those achieved by the default approach with equally spaced beam setting. It is effective to find the optimal beam combination of IMRT plan with the intelligence-guided approach.